{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.7. Creating a route planner for a road network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import zipfile\n",
    "import requests\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "import smopy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = ('https://github.com/ipython-books/'\n",
    "       'cookbook-2nd-data/blob/master/'\n",
    "       'road.zip?raw=true')\n",
    "r = io.BytesIO(requests.get(url).content)\n",
    "zipfile.ZipFile(r).extractall('data')\n",
    "g = nx.read_shp('data/tl_2013_06_prisecroads.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "464"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgs = list(nx.connected_component_subgraphs(\n",
    "    g.to_undirected()))\n",
    "i = np.argmax([len(sg) for sg in sgs])\n",
    "sg = sgs[i]\n",
    "len(sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos0 = (36.6026, -121.9026)\n",
    "pos1 = (34.0569, -118.2427)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_path(n0, n1):\n",
    "    \"\"\"If n0 and n1 are connected nodes in the graph,\n",
    "    this function returns an array of point\n",
    "    coordinates along the road linking these two\n",
    "    nodes.\"\"\"\n",
    "    return np.array(json.loads(sg[n0][n1]['Json'])\n",
    "                    ['coordinates'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from https://stackoverflow.com/a/8859667/1595060\n",
    "EARTH_R = 6372.8\n",
    "\n",
    "def geocalc(lat0, lon0, lat1, lon1):\n",
    "    \"\"\"Return the distance (in km) between two points\n",
    "    in geographical coordinates.\"\"\"\n",
    "    lat0 = np.radians(lat0)\n",
    "    lon0 = np.radians(lon0)\n",
    "    lat1 = np.radians(lat1)\n",
    "    lon1 = np.radians(lon1)\n",
    "    dlon = lon0 - lon1\n",
    "    y = np.sqrt((np.cos(lat1) * np.sin(dlon)) ** 2 +\n",
    "        (np.cos(lat0) * np.sin(lat1) - np.sin(lat0) *\n",
    "         np.cos(lat1) * np.cos(dlon)) ** 2)\n",
    "    x = np.sin(lat0) * np.sin(lat1) + \\\n",
    "        np.cos(lat0) * np.cos(lat1) * np.cos(dlon)\n",
    "    c = np.arctan2(y, x)\n",
    "    return EARTH_R * c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_path_length(path):\n",
    "    return np.sum(geocalc(path[1:, 1], path[1:, 0],\n",
    "                          path[:-1, 1], path[:-1, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the length of the road segments.\n",
    "for n0, n1 in sg.edges:\n",
    "    path = get_path(n0, n1)\n",
    "    distance = get_path_length(path)\n",
    "    sg.edges[n0, n1]['distance'] = distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = np.array(sg.nodes())\n",
    "# Get the closest nodes in the graph.\n",
    "pos0_i = np.argmin(\n",
    "    np.sum((nodes[:, ::-1] - pos0)**2, axis=1))\n",
    "pos1_i = np.argmin(\n",
    "    np.sum((nodes[:, ::-1] - pos1)**2, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the shortest path.\n",
    "path = nx.shortest_path(\n",
    "    sg,\n",
    "    source=tuple(nodes[pos0_i]),\n",
    "    target=tuple(nodes[pos1_i]),\n",
    "    weight='distance')\n",
    "len(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "podoc": {
     "output_text": "Output"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sXdnaU10L7JAtarUX6teqZg/WlLOdIKTt2xLO/asZrnxzHUc+P9tFqnFifHyjqy0rvEpR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\n/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o+DGA8AM0OMB9AWMX4cxHgAmBliPIC2iPHjIMYDwMwQ4wG0RYwfBzEeAGaGGA+gLWL8\nOIjxADAzxHgAbRHjx0GMB4CZIcYDaIsYPw5iPADMDDEeQFvE+HEQ4wFgZojxANoixo+DGA8AM0OM\nB9AWMX4cxHgAmJlhMf7n5+kvhzswvb2Vl9f1Xo5M/faW/+u4fX0dttTH9vW1WFxfn/3l+nqx+Prq\nmnq9zu/J9frl5fZW53J7m5/i6UmXc3m5WCyX6RTL5WJxeanr8fS0Xo9dMvv09A+5chD6fauph4eL\n8W9v3Xt4+0+UatWQOlGax75qFTEeAGZmWIy3A6D/2tPTWdHu0VOXOPy9HD105/86blrG3Vu2v31w\nWOu11TLf/X057CwWuT3586NBybm+DuexXtupgjo///mJ53F+3jWH02Jb+fKSe3e9tvf753SYGJ/W\nlNvb7v0z5BP5WjWsTtzf52tm1xzK3+T+XytiPADMzJAY//aWO5AT4/dvTjHeovXtrbb9WvC6vs5P\n//KS25PrtUalxeLj4+vr7U3nGs5DX7u/lykktEmQ91uil0udx9PT19fHh65HaS1OgYuVuXfz3/68\nw8R43SPSpi57WE/a7u/bfCJfq4bVCa1T8Tz65kCMBwBUq4/x7jBOjD+0mhh/efmU1d0h5dhofArb\nJ5dLDdy52qRRKd2T+rrfoUFaRv2WZq3Pbp7692IRz8OVXzrP0+K+t7luNVp+xxPj0/2la1jeP/Wf\nKNWq+jqxXnfXzPIcct9hPeHor3nEeACYmboYL308/XgeHhxvI9aemnZSGI4YL2pi/FS2pZvUnfPz\nuJuCtq2fn4evLpeui0S89TJ9+Jp2CnHtnhKNLi/9KTRQWYRdLuPQl/vMKdFylJJLu9Vo6Wmc7J/T\nIWJ8WlO0lpSvl9R9oqtW1daJry/XXSucxza1Kj39KCHGA8DM1MR4u/ycj/EpOwzuHuKJ8Wo+Mb60\nJRqxXY2SE0vrNpN+5ucnbHlXfn3RKcL2/Y8P/1N6XeDjw59C90OLen2MtBylPNMoLFHy+rr2G7f/\nGJ+Lw/GJ2PBPdNequjoht6/q719uHsNrlfakr+vMRYwHgJmpifEW3l0LU9fU1m6fv1FuqKEx3h+H\nIh4DohTjX15y42/o6DD/9m/yv+EhNh3jRsbwcNsbjpDy8pLeAudGSDk/z42X4d6XkSx2j/Hh+rmt\nC3uC65Z2lca+b/D8+tI23/Qd6UXsjyWjU56fy1blYrxcE4q7E/n1RVs4wzilLc7WV1pDWLjFYdA/\nNVqOWjJxFJbuJ1ar++e0/xivaxl/c3T/5Duf1Hyiu1bV1Qn9rp6fS91K5zG8VmmZ1506EuMBYGbq\nY7yM6dAf47WVs13b8JAYn45Ncn7uH7ZLMT5u6VUSoy8vdZv9Njw96IbrJO1lNk26FpeX4bzf3sKR\nKuIp0pEsdo3xaStl2ttc2wmtLTFfGm32aZnVsP57KiRwLRYah+rqm3UL0b+0ROOg788pV/M05k3t\npuFa/rc8DJX6mnU36Z/T/mN8fv917Z+aT/TVqpo6Ie35dsKbzmNordJvZW2NI8YDwMzUxfjLS43D\n/THe2phLY08PVR/jLfzqOBQfH3Zp27W4lmK8nnqEl9v1EryEmTjA2tUGFwj087qccC3s2oB/l4Dd\nKmxjqNgUVmI2B33fLvLvFuPjVkqLy/5nZD3k0n13aeybla7csNvVArleuxpWF+N1zhaI9ObGeBq/\nRqW96+uXNU22bVI2YScOqbNyleJ4Yrzuv/TqkLyaH3um5hN9taqmTiyXbhnpPIbVKrsnpPYqGDEe\nAGamrm+8f7jpOpBbRG3XXpkbQzwUTukvWcOrOwiWYrzeZBbeQKmhTw7q2h7mIqV1LfLb39yndS38\nawDh0HU2EKLfnUOXFbYC+5HbWvfLpdQfLv3rBe6Kgpun3wbbVRr7Z6dfdqLRHefrtt6dHNk25AOp\n/2p+rnOI8Wm3GonAUiOPJ8aX1qS8f4Z+Ivf60DqRvjNsDvq9qz99JsYDwMwMe/xTX4zXwFnfetSv\nNsZrG3J8KAwPg+UYn954JluibXRhS7T+JWvl5iTBXqfNr4XfuzV/kdxNkbsRTy+6bzduvE0hQcxF\nc320jbxv1xQ02PvrmC+NQ0hvqZa2+XKd6o/W9qAnV+7E+JRtW9ytRuuklD8x/pAxXk/5h3RlI8YD\nwMy0jPHt2+JdG3t5NGWdTgN7eMuiBRI7QJZjfHhzo4Vam5sfYmUbLy816OorGnK09Ty/Fjo3LRXd\nnjiSuilyI1nY6VH/finHeN031q4t89Me+ravJNhbYOgujcP4+XEPfrIoX2qV74vWFuL9k6O6GJ/W\n5DnE+LhbjdRJrQ3HFuPTZyL0xfj6T5Ri/JA6kY/xtXMIu4HVIMYDwMy0jPHWhtpyPJPavvGlnq8a\n4HIHy/Cv8PMSal3btX+DqbyzWGiru0YCDd76yfxa+CcT+cHj3BS5kSzs1e79kn/8U7gE/VtPPJZL\n/1HxfqTvLo3DkrsD3HMI8t16uqO1hfjwkVLE+JTbNj3ps/KS8tPTVGL8IWO8dm4b8mtKjAeAmWkX\n463rR81jSurVxvjSdOVuEuFf/k2gfuR1W6YBQOKuTOVir0Rea7nuv3DfFxryc2gxbrzrCCQnHnIq\n4a4p6GmJa+3uKo0x2ON08nWra+vdLcdhHCrf4monLOGJjb+sw3UvOixXjrrPdf+7LjXHFON1/+VD\nedctrvWfyNWqoXUinUf9HPQ7OOzXlBgPADPTLsanI8O0cKgYr21f2k4edkARdvDVuCuRxj0kR16x\nfsTHHOPdVQMJNG5rZH/5N+n2l8YYNEzm+wmXt96N+pMv0bh135+T7Ik4Ws1hwEn9tzs5dV1qjinG\n52/+7B9wsv4TuVo1tE6k86ifg/6eDvs1JcYDwMy0i/E2MOKQ+fXbtVONP+pKd4x3wc6GXgwPqdZ+\n7Yfb9VrbzCwO5tdCI6gevPOdatwU+b7zLWK8LkNigayDxgMpHYkP0tYdRt2u0tiX/HM2TbkmlLbe\nDe1Zei9smw2fEaC3AYef6nq80PT55ei61bguNccU43X94ujb//in+k/katXQOpHOI/eM3Pwc0vGi\n+hHjAWBmWsX41o99MrUxPn9zqY0rkzuoxmtrI9Lkxki3sG7t2C4Uh4flbW9xdREjP4d8F5Bwv/SX\nvASypydt+9M10LV3Ab+uNPana5RsWft8rMlvvXXyyreTan0N3wufpunf82B074x9ZWJf/HK0bjV+\nl5pjivG6XnEg7hoYdegncrVqaJ1I51E7h9zTG/oR4wFgZlrFeD08te9wUBvjdxtw0s3v+jp3oLVQ\n49qxrSVb/teF3N0HnMzNwcaa794vdQ9AkhthXZjR0wdZpzQgl0tjf7QjQS68aCiv7YEsoU3LrNxy\nLnsu7KQTPus2PYEZPgDgtITlqN1q/C41xxTj7eZ1/3qK7p/ylaNhn8jXqmF1Ip1Hbg5pTbRv5tDr\nPsR4AJiZVjF+Pz3jhzzFVf/yI6AeCmse/+RPn3YwEToEnx9ptT953I6WPoRKTyUsKlgk9w/QWnb+\no6jC9+3Jpt37pT/G6ymCdaQRGipkjdKA3FUa+2Lh+/4+bB/9+NDX0xsUS1uf7oeYnqC4bdPt9bdV\nx9p3e1f306l2qYnLUcvDHvykjinGW/10p5h9+2fYJ/LfqWF1IjePujnoq0Ov+xDjAWBmWsV4G+O7\ndYeD+hjvxiR5e/v60u4u4QGyL8ZbxM6djFg/63hsm7gdLVyLtzddR/+w7Xpsf3zkprDOILe3Hx/r\nP1mI3z3Gu/sXXBy2wRzTINJVGvtjpSNxS4fMXCxsINNSKE+33nWoSbkTBN32+3vZD1pbwiEt9aTi\n/Fw6In186H46zF0C4wjLUa9AhddijinG29rIUxzke6R7068H6bem7xPl0jDD6kT5OlHfHGpLOkSM\nB4CZaRXj+6PmdupjvETo+Omf5+d+PO2L8dYGlrtEru3WfpS0mBOHy3Qt4gcXaeec8hR+lLVA2ybG\n26mNe8VOEnLdZsqlsU9fX3H5pXuyb+ttO3PcKcxyaScxtoz4JPTrK9xT4djzpyYuR70jw79Oc1wx\nfr2On/EcDiua/iL1faKrNMyQOpGfR80ctuu+RYwHgJlpHePzt5dtb0iMl8P0y4sdqG9vX17Cw2N/\njM/d+GjSnrX6Snr9IVyLt7f0IL1c2kONzs/v79OA6qKsRPj1Wtrsu2J6bYzXKwj+lPpKvs95V2ns\n19vbYuEe+pTuyb6tj8NaPsbLnrL9cHm5WOTq7nJpVwN0Txy+LA4nLkftduTXzuOK8cKuZuk3Ld2e\ndG27PtFVGk59nSjNo38Otd/oEDEeAGZmWIw/ddoO3vpUZDs/P/me4PMsDUzJ4WI8fMR4AJgZYrzP\nPekUlAa2RYwfBzEeAGaGGO/ojZGnOxLJMJQGtkWMHwcxHgBmhhgvdESU0x4VnNLAoRDjx0GMB4CZ\nIcYLu+ktHatkjigN7IYYPw5iPADMDDFeaHC9vSW2CkoDuyHGj4MYDwAzQ4wX6/XYo8IcE0oDuyHG\nj4MYDwAzQ4wH0BYxfhzEeACYGWI8gLaI8eMgxgPAzBDjAbRFjB8HMR4AZoYYD6AtYvw4iPEAMDPE\neABtEePHQYwHgJkhxgNoixg/DmI8AMwMMR5AW8T4cRDjAWBmiPEA2iLGj4MYDwAzQ4wH0BYxfhzE\neACYGWI8gLaI8eMgxgPAzBDjAbRFjB8HMR4AZoYYD6AtYvw4iPEAMDPEeABtEePHQYwHgJn5448/\n7kf7759GXDb/sTcpjX3998+jr8Es//v7v9T+7hPjAQAAgMkhxgMAAACTQ4wHAAAAJocYDwAAAEwO\nMR4AAACYHGI8AAAAMDnEeAAAAGByiPEAAADA5BDjAQAAgMkhxgMAAACTQ4wHAAAAJocYDwAAAEwO\nMR4AAACYHGI8AAAAMDnEeAAAAGByiPEAAADA5BDjAQAAgMkhxgMAAACTQ4wHAAAAJocYDwAAAEwO\nMR4AAACYHGI8AAAAMDnEeAAAAGByiPEAAADA5BDjAQAAgMkhxgMAAACTQ4wHAAAAJocYDwAAAEwO\nMR4AAACYHGI8AAAAMDnEeAAAAGByiPEAAADA5BDjAQAAgMkhxgMAAACTQ4wHAAAAJocYDwAAAEwO\nMR4AAACYHGI8AAAAMDnEeAAAAGByiPEAAADA5BDjAQAAgMkhxgMAAACTQ4wHAAAAJocYDwAAAEwO\nMR4AAACYHGI8AAAAMDnEePx9tXp+vrsbey0AAABQb3CM32yeny8uvr/LU2goPPvLzc3z82o19kZO\nxfOzlNlhl/n5aftq7K0HAABAvUHhTSO8RL5yjH9/lykeH9/fv78/P3X65+exN3MKvr8PHadXq5sb\nC/HEeAAAgCmpDm8uwnfF+Pf3s7OLi99f98rvL0G+xmZzdTVOnNZrAMR4AACAKakOb6+vn5+r1eNj\nV4xfrSSyv7+Hr0q072q/h7Awffg4ffirAAAAANjVwPD2+9sV419fc3FwtZJXHx/H3tRj9vl5dvbw\nQIwHAABAncHhrSvGaz/r/GdObySUzWa793JTX1zc3Y0Vp4nxAAAA07OHGP/5Gb662Zxia/xmc3NT\n2ia5HyDuWtTl4eHiYrUixgMAAKBW0xivPefjdnfpMHKKfeNla3NBXkJ8eJtvN+mKJKc+xHgAAADU\nahrjLRCG4Vba6E+vS43IBfmhIV5uC354kH8R4wEAAFCraYy3bjVyu6b1Dpeoe3NT01f8/V0+Lf/6\n/r65OTu7ugrD8Pf346MOynh19fAQdlqxEXSUDm/pD5Apw2C6QS/dWOluLt/feouphGoZlefioqY0\n4iA/NMT//e83N1dXWjrD4/T7u3TH0cdsPT7mH7Plprm7e33N7Yd4uXd3NvSl28+/v3d3rjT9T//+\nPj7a48BkJCOZ6uLCX5vPT10DeTVevv9p2QMy3dVVbnjS7fYPAADAqWoc4+VmTXt+q4RZjbl9IV6e\n+2qfswEqNbK5+UqIu7uTh0pp3LclmO9vm4P/qsV7be92pKOPH7dlmXd3n58yd/tMXXn4QX54iJdh\nJq0sh8X439+rKzk1+f7T66tuexx/v78lkD8+yjS6Vbk++7nl6phD8X6Ox9KxPaHTuSEzdUlSDrrf\nHHdCt9m8v8vJmn5ankrgTxdf49h+/wAAAJymxjHeHvdk7aYXF/ENrylpw3UxXlriv78tHuoUcjtp\nGO0slPqR2eK/3yptpxWvr+EyZVq3DRJk/aCvc6otEQvyw0O8DN/poveQGC/PyvWXZZ/1g3x8T4IN\nFhoH+dxy9bbkeD/rHG3K19fnZwvi2uIuJxXyL33t7k72m7Stu9MIt4bPz/6n5V93d/KaXQnw681u\n+wcAAOAUNY/xFrrzraplFsJvbjSGS+yzyCeh/uoqnF6D/sWF39KvETCM7Bof49Z46Z7j/pLgGW6R\nfKq+TGRqibHDQrw8t9XvblQf4/VUKTxBiq9f6KO4wvK3Nvbwk/nl5vZzOqWdGkiEd1tiQf7qyr8S\no+3tfmcY92l3ZcWeZuvvsV33DwAAwOnZQ4z3W+Rr+8W7iJh2+tCAH7en526n1aB4c5ObLmyjD+eX\nzl8eWTWkVDS4DgnxurZht6DaGN91WmN/6+lLvEZ2b0GuhMIp62K8TReWnk0Xvmqh3V+n3Mme7kV/\nHXffPwAAAKdmDzFeeqnf3LjbSOuCfDnEavRMl2ht/i6g6/Ni08gedzeREwN/mrRdX2JwfZnYacuQ\nz8j2hn3Za2O8Thf3g99spJOLbZWVRPxZ64Hunyy1iPHx3ql9tb/rTov9AwAAcHqax3iJyBrc3egx\nNUG+FGKtDTf9hEVSv51WTx7iyB637t7chJ1s9IRg2COb/DXU7jSlceRz9LmtdSUQ03LtvudAu8+k\nw3xaafrbf2wxPl3KbvsHAADgFDWO8RqarduEG32kf9z4Uoi1eZQ/4UdnXb4f2WVwynCdJcqGIdj6\njMsnh0ZF/8bW+iCvz22tK4GYhtrubk16OpMr9fSk5vhj/C77BwAA4DQ1jfHpbZVu8Mi++FUKseGY\nNT7rLuOHVRuZxmK6dp/R9mtbLxkVJ56XP+78kKgYj05TF+R1uMu7iHUS0r/K69B3PcRNk4vxtpS+\nkj+mGL/9/gEAADhVTWO8hq3wPWtNj8eKiQ2P8fmwGkZ2eSyS622tXXv8B0E5n5/uoUduvJxuuSEm\na4J8OEZ6Xm4d/a3ujrLlGJ+W5xRi/Hb7BwAA4HQ1jfHaEh6/Gj80KK8vxueWqO+Eodl6f0tkl3nq\n59xQlBLp8xFQHkHkRthJO73ESuPE9wf5FjG+/L6bJvekUy3PaXWq2W7/AAAAnLKmMT4fv0qjpoT6\n+sbnWp/z72hkl1elc4o/FwmvDw9d/fT9p4l29+eX7julceL9Me/r1faNzw0aad7ftTuRtVynUVdj\nvH+aMZUYP2z/AAAAnLYDtMZrf+xcy7CvFN6sd33avm194+OoaqO0yOmD9ZG3Uwlpi09PCMJI/Pub\nDmWZ8/5eHifefxhSrdoYH9+y65fI1ZV7eFb+5Ee37ZgHnEyXst3+AQAAOGVNY7wGzPQ9aQHu6y9e\n7kqhJwfxuOH2iXS+Ftnv7uIntcpc0vnINoWB0DrmdN9G2lptjHfPu43feXy01+xugPSOhLSzTX65\nWurHEeOPY/8AAAAck6YxvhSsJRL2PeG0HGKtG0XcTUVfz7XIWnu13+ZcbtWXbYrnrV1Phj2VdVe1\nMd5ObMJt2WykBd5tcb7FWgNw/pmruRLwy2WzSUe5KfXUbx3jj2H/AAAAHJOBMd46spTGSdFoHb4r\nnVz6hwi08JZGM+kqom3I4dNZ5dUwkBqN7HE3Hg2/ueiXvi4xMd/7fH9qY7w/jvrdnXQben9/fJSt\n83uL29zScXziPuXpU1OF3U1g1y6+v29ubLlu1H2rD/HJUVeMTz+dj/Huqslx7B8AAIBjMjDGW5B7\neCj1/db49/xskfv9ve7pm9aCnpuzjAmj3UhsrpuNBLnybaQyfa71Ph/9NPS7oKht1t3PSW2vPsaH\n46ibuLtQev3BPWHXZ2MB5dvoZXjO5+ebGykf99rdnc7V6kO47Hwtca+WXxFWE9z6HMf+AQAAOCbV\nMf739+7ODfen7bz5GL1aSeyTyPz4p5qbPdM5x23m7rZGmaMMPHh319UzWqaIlyt95vOt97bcx8fP\nz+9vHdbw8I8YGhLj00ErcyOpyymUtqlbFE87p4Sjsfvv+0vQEO1a+KV03t9tn/iv6r53n9N9GS5H\nXg0/ra/E08l6H8/+AQAAOCaD+8aP5/394eHiQqLd62vfGCWrVS7mvb7mTykeHr6/pWOKBks5/ZjC\nGCi/v4+PGnrLz3y1Uyo5+Rl+Q+jrq8z/4sLKQ2L84+Phe6RPc/8AAADs04RiPAAAAABFjAcAAAAm\nhxgPAAAATA4xHgAAAJgcYjwAAAAwOcR4AAAAYHKI8QAAAMDkEOMBAACAySHGAwAAAJNDjAcAAAAm\nhxgPAAAATA4xHgAAAJgcYjwAAAAwOcR4AAAAYHKI8QAAAMDkEOMBAACAySHGAwAAAJNDjAcAAAAm\nhxgPAAAATA4xHgAAAJic/x9GQfjgwKoDAgAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAxNy0xMS0yN1Qx\nNTo1Nzo0NyswMTowMG4aAGwAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMTctMTEtMjdUMTU6NTc6NDcr\nMDE6MDAfR7jQAAAAIHRFWHRwZGY6SGlSZXNCb3VuZGluZ0JveAA1OTV4ODQyKzArMDsfVVwAAAAf\ndEVYdHBkZjpWZXJzaW9uAFBERi0xLjQgMSAwIG9iaiA8PCCeMHjFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "Output"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roads = pd.DataFrame(\n",
    "    [sg.edges[path[i], path[i + 1]]\n",
    "     for i in range(len(path) - 1)],\n",
    "    columns=['FULLNAME', 'MTFCC',\n",
    "             'RTTYP', 'distance'])\n",
    "roads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "508.664"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roads['distance'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = smopy.Map(pos0, pos1, z=7, margin=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_full_path(path):\n",
    "    \"\"\"Return the positions along a path.\"\"\"\n",
    "    p_list = []\n",
    "    curp = None\n",
    "    for i in range(len(path) - 1):\n",
    "        p = get_path(path[i], path[i + 1])\n",
    "        if curp is None:\n",
    "            curp = p\n",
    "        if (np.sum((p[0] - curp) ** 2) >\n",
    "                np.sum((p[-1] - curp) ** 2)):\n",
    "            p = p[::-1, :]\n",
    "        p_list.append(p)\n",
    "        curp = p[-1]\n",
    "    return np.vstack(p_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "linepath = get_full_path(path)\n",
    "x, y = m.to_pixels(linepath[:, 1], linepath[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "podoc": {
     "output_text": "<matplotlib.figure.Figure at 0x14957cc0>"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yJIpaPL+qoJr171mSjxx/uORL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14957cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = m.show_mpl(figsize=(8, 8))\n",
    "# Plot the itinerary.\n",
    "ax.plot(x, y, '-k', lw=3)\n",
    "# Mark our two positions.\n",
    "ax.plot(x[0], y[0], 'ob', ms=20)\n",
    "ax.plot(x[-1], y[-1], 'or', ms=20)"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 2
}
